Operational weather forecasts based on NWP rely on decades-long research using laws of physics for atmospheric simulation. While the tremendous increase in observational data, scientific and computing advancements has resulted in an increase in the forecast skill by about one day every decade [1]}, the recent success of deep learning in scientific domains has spurred an interest in its application to weather forecasting [2]}, [3]}.
For the domain of nowcasting, Prudden et al. [4]} provide a detailed review of radar-based nowcasting techniques and several ML approaches used in the past. Shi et al. [5]} applied a recurrent neural network based approach using convolutional LSTMs [6]}. Agrawal et al. [7]} use a U-Net model and turn the forecasting problem into an image-to-image translation problem. Trebing et al. [8]} apply attention modules and show a reduction in model parameter size while maintaining performance. Ravuri et al. [9]} use a generative model that is radar-based only. However, all of these techniques have been shown to be skillful only from 0 to at most 3 hours of lead time. The MetNet model [10]} shows initial results for for skilful nowcasting of precipitation up to 7-8 hours over a HRRR baseline. On the longer forecast sides, deep learning has also been applied for seasonal forecasts of weather events including extreme ones [11]}, [12]}.
